Microwave sensors can be applied to a variety of industrial measurement problems. Often, however, it is necessary to sift through large amounts of data in order to extract the parameter of interest from a given sensor's output. At Baylor University in Waco, TX, Eric C. Green, Buford Randall Jean, and R.J. Marks II developed a method for calibrating a microwave sensor. This method utilizes an artificial neural network, which is trained to infer the consistency and conductivity of pulp stock slurry from the measured output of a microwave instrument. Through its ability to recognize patterns, the neural network was able to automatically sort between needed versus useless data. It could therefore obtain a meaningful result without requiring the input of an expert. The network proved itself particularly useful in the interpretation and analysis of microwave spectrometry data. See "Artificial Neural Network Analysis of Microwave Spectrometry on Pulp Stock: Determination of Consistency and Conductivity," IEEE Transactions on Instrumentation and Measurement, December 2006, Vol. 55, No. 6, p. 2132.